We are looking for a skilled AI Data Engineer to join our team and play a key role in building and maintaining scalable data platforms and AI-ready pipelines within banking and payments systems.

⚙️ Key Responsibilities Build and maintain data pipelines (batch, micro-batch, real-time) for banking and payments data Develop AI-ready datasets for ML use cases (fraud detection, credit scoring, AML/KYC) Implement data ingestion frameworks from multiple sources (core banking, APIs, payment gateways) Design and manage data storage solutions (Data Lake / Lakehouse / SQL & NoSQL) Ensure data quality, consistency, and validation across workflows Support feature engineering and ML pipelines Implement streaming solutions (Kafka / Event Hub) for real-time processing Optimize performance for handling large-scale transactional datasets Ensure compliance with data security and regulations (PCI DSS, GDPR) Contribute to CI/CD pipelines and Dev Ops practices
????️ Required Skills✅ Core:Strong experience in Python & SQLHands-on experience with Spark / Databricks✅ Data Engineering Tools:Azure Data Factory (ADF) Airflow / DBT / SSIS✅ Streaming:Kafka / Event Hub✅ Cloud:Azure (preferred) AWS / GCP✅ Knowledge:Data modeling (OLTP / OLAP) AI/ML data preparation pipelines Handling structured & unstructured data
???? Domain Expertise (Must Have) Experience in Banking / Fintech / Financial Services Strong understanding of:Payment flows Transaction processing ISO 20022 / SWIFT / Card systems Exposure to:Fraud Detection AML / KYC datasets
???? Qualifications Bachelor’s in Computer Science / Data Engineering or related field3–5 years of experience in Data Engineering Banking experience is highly preferred
⭐ Nice to Have Delta Lake / Lakehouse architecture Real-time analytics / event-driven systems Docker / Kubernetes / Dev Ops Data governance tools